Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Mathematical statistics --- Statistical matching --- Sampling (Statistics) --- wiskundige statistiek --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Random sampling --- Statistics of sampling --- Statistics --- Statistical matching.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census.  Contents Regression Models Including Selected Small Area Methods Statistical Matching Application to Poverty Estimation Using EU-SILC and Micro Census Data Bootstrap Methods Target Groups  Researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics  The Author Verena Puchner obtained her master’s degree at Technical University of Vienna under the supervision of Priv.-Doz. Dipl.-Ing. Dr. techn. Matthias Templ. At present, she works as a data miner and consultant.

Mathematics. --- Computational Mathematics and Numerical Analysis. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Computer science --- Distribution (Probability theory). --- Mathématiques --- Informatique --- Distribution (Théorie des probabilités) --- Computer science -- Mathematics. --- Poverty -- Statistical methods. --- Mathematics --- Physical Sciences & Mathematics --- Mathematics - General --- Statistical matching. --- Statistics --- Formal methods (Computer science) --- Income distribution --- Poverty --- Social sciences --- Data processing. --- Research --- Mathematical models. --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Destitution --- Distribution of income --- Income inequality --- Inequality of income --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Applied mathematics. --- Engineering mathematics. --- Computer mathematics. --- Probabilities. --- Wealth --- Basic needs --- Begging --- Poor --- Subsistence economy --- Distribution (Economic theory) --- Disposable income --- System design --- Civilization --- Sampling (Statistics) --- Distribution (Probability theory. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Engineering --- Engineering analysis --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Probability Theory.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Understanding the mechanisms involved in life (e. g. , discovering the biological functionofasetofproteins,inferringtheevolutionofasetofspecies)isbecoming increasinglydependent onprogressmade inmathematics,computer science,and molecular engineering. For the past 30 years, new high-throughput technologies have been developed generating large amounts of data, distributed across many data sources on the Web, with a high degree of semantic heterogeneity and di?erentlevelsofquality. However,onesuchdatasetisnot,byitself,su?cientfor scienti?c discovery. Instead, it must be combined with other data and processed by bioinformatics tools for patterns, similarities, and unusual occurrences to be observed. Both data integration and data mining are thus of paramount importance in life science. DILS 2007 was the fourth in a workshop series that aims at fostering d- cussion, exchange, and innovation in research and development in the areas of data integration and data management for the life sciences. Each previous DILS workshop attracted around 100 researchers from all over the world. This year, the number of submitted papers again increased. The Program Committee - lected 19 papers out of 52 full submissions. The DILS 2007 papers cover a wide spectrum of theoretical and practical issues including scienti?c work?ows, - notation in data integration, mapping and matching techniques, and modeling of life science data. Among the papers, we distinguished 13 papers presenting research on new models, methods, or algorithms and 6 papers presenting imp- mentation of systems or experience with systems in practice. In addition to the presented papers, DILS 2007 featured two keynote talks by Kenneth H. Buetow, National Cancer Institute, and Junhyong Kim, University of Pennsylvania.

Bioinformatics --- Statistical matching --- Computational biology --- Bio-informatique --- Appariement (Statistique) --- Congresses. --- Congrès --- Congresses --- Data integration (Computer science) --- Computing Methodologies --- Medical Informatics Applications --- Biology --- Medical Informatics --- Biological Science Disciplines --- Information Science --- Natural Science Disciplines --- Disciplines and Occupations --- Mathematical Computing --- Computational Biology --- Information Systems --- Computer Simulation --- Engineering & Applied Sciences --- Health & Biological Sciences --- Computer Science --- Biology - General --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Computer science. --- Health informatics. --- Database management. --- Data mining. --- Bioinformatics. --- Computational biology. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Health Informatics. --- Database Management. --- Information Systems Applications (incl. Internet). --- Computational Biology/Bioinformatics. --- Computer Appl. in Life Sciences. --- Bio-informatics --- Biological informatics --- Information science --- Systems biology --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- Clinical informatics --- Health informatics --- Medical information science --- Medicine --- Informatics --- Science --- Data processing --- Sampling (Statistics) --- Medical records --- Data processing. --- EHR systems --- EHR technology --- EHRs (Electronic health records) --- Electronic health records --- Electronic medical records --- EMR systems --- EMRs (Electronic medical records) --- Information storage and retrieval systems --- Medical care --- Database management --- Application software. --- Bioinformatics . --- Computational biology . --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Medical informatics.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences.  These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative.   Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

Algorithms. --- Context-aware computing. --- Engineering. --- Multisensor data fusion. --- Systems engineering. --- Wireless sensor networks. --- Context-aware computing --- Multisensor data fusion --- Wireless sensor networks --- Statistical matching --- Engineering & Applied Sciences --- Electrical & Computer Engineering --- Electrical Engineering --- Computer Science --- Computer algorithms. --- Statistical matching. --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- WSNs (Sensor networks) --- Circuits and Systems. --- Signal, Image and Speech Processing. --- Algorism --- Algebra --- Arithmetic --- Engineering systems --- System engineering --- Engineering --- Industrial engineering --- System analysis --- Foundations --- Design and construction --- Sampling (Statistics) --- Algorithms --- Computer networks --- Low voltage systems --- Sensor networks --- Wireless communication systems --- Electronic circuits. --- Signal processing. --- Image processing. --- Speech processing systems. --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Electron-tube circuits --- Electric circuits --- Electron tubes --- Electronics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data management and data integration are fundamental problems in the life sciences. Advances in molecular biology and molecular medicine are almost u- versallyunderpinned by enormouse?orts in data management,data integration, automatic data quality assurance, and computational data analysis. Many hot topics in the life sciences, such as systems biology, personalized medicine, and pharmacogenomics, critically depend on integrating data sets and applications producedby di?erent experimentalmethods, in di?erent researchgroups,andat di?erent levels of granularity. Despite more than a decade of intensive research in these areas, there remain many unsolved problems. In some respects, these problems are becoming more severe, both due to continuous increases in data volumes and the growing diversity in types of data that need to be managed. And the next big challenge is already upon us: the need to integrate the di?- ent “omics” data sets with the vast amounts of clinical data, collected daily in thousands of hospitals and physicians’ o?ces all over the world. DILS 2006 is the third in an annual workshop series that aims at fostering discussion, exchange, and innovation in research and development in the areas of data integration and data management for the life science. DILS 2004 in Leipzig and DILS 2005 in San Diego each attracted around 100 researchersfrom all over the world. This year the number of submitted papers again increased. The Program Committee selected 23 papers out of 50 strong full submissions.

Bioinformatics --- Statistical matching --- Computational biology --- Bio-informatique --- Appariement (Statistique) --- Congresses. --- Congrès --- Congresses --- Data integration (Computer science) --- Computational Biology --- Information Systems --- Systems Theory --- Science --- Biology --- Medical Informatics Applications --- Natural Science Disciplines --- Medical Informatics --- Biological Science Disciplines --- Disciplines and Occupations --- Information Science --- Biology - General --- Computer Science --- Engineering & Applied Sciences --- Health & Biological Sciences --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Computer science. --- Health informatics. --- Database management. --- Information storage and retrieval. --- Bioinformatics. --- Computational biology. --- Computer Science. --- Information Storage and Retrieval. --- Health Informatics. --- Database Management. --- Information Systems Applications (incl. Internet). --- Computer Appl. in Life Sciences. --- Bio-informatics --- Biological informatics --- Information science --- Systems biology --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- Clinical informatics --- Health informatics --- Medical information science --- Medicine --- Informatics --- Data processing --- Sampling (Statistics) --- Database management --- Information storage and retrieva. --- Medical records --- Data processing. --- EHR systems --- EHR technology --- EHRs (Electronic health records) --- Electronic health records --- Electronic medical records --- EMR systems --- EMRs (Electronic medical records) --- Information storage and retrieval systems --- Medical care --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Application software. --- Bioinformatics . --- Computational biology . --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software